混合扩散与协同过滤，打破推荐系统准确率与多样性矛盾。
HI-Series Algorithms A Hybrid of Substance Diffusion Algorithm and Collaborative Filtering
- 用非线性组合融合物品协同过滤与扩散算法，动态调节性能平衡。
- 稀疏数据下F1提升0.8%~4.4%，密集数据下提升2.3%~5.2%，多样性显著提高。
- 适合追求高多样性与鲁棒性的推荐系统设计者，尤其在数据稀疏场景中表现突出。
推荐系统面临准确率与多样性的权衡难题，传统基于协同过滤（CF）和基于网络扩散的算法各有局限。尽管基于物品的协同过滤（ItemCF）通过物品相似性提升多样性，但牺牲了准确率；而大规模扩散（MD）算法虽侧重准确率、偏好热门项目，却缺乏多样性。为此，本文提出HI系列算法，将ItemCF与扩散方法（MD、HHP、BHC、BD）通过参数ε控制的非线性组合进行融合，兼顾ItemCF的多样性与MD的准确率，并扩展至高级扩散模型（HI-HHP、HI-BHC、HI-BD），实现性能提升。在MovieLens、Netflix和RYM数据集上的实验表明，HI系列算法显著优于基础模型。在稀疏数据（20%训练）条件下，HI-MD相较MD在F1分数上提升0.8%~4.4%，多样性（Diversity@20）达459（原为396）。在密集数据（80%训练）下，HI-BD相较BD提升F1 2.3%~5.2%，多样性最高提升18.6%。值得注意的是，混合模型在稀疏环境下持续提升新颖性，且对参数适应性强。结果验证了策略性融合可有效突破准确率-多样性权衡，为不同数据稀疏度下的推荐系统优化提供灵活框架。
原文摘要 · Abstract (English)
Recommendation systems face the challenge of balancing accuracy and diversity, as traditional collaborative filtering (CF) and network-based diffusion algorithms exhibit complementary limitations. While item-based CF (ItemCF) enhances diversity through item similarity, it compromises accuracy. Conversely, mass diffusion (MD) algorithms prioritize accuracy by favoring popular items but lack diversity. To address this trade-off, we propose the HI-series algorithms, hybrid models integrating ItemCF with diffusion-based approaches (MD, HHP, BHC, BD) through a nonlinear combination controlled by parameter $ε$. This hybridization leverages ItemCF's diversity and MD's accuracy, extending to advanced diffusion models (HI-HHP, HI-BHC, HI-BD) for enhanced performance. Experiments on MovieLens, Netflix, and RYM datasets demonstrate that HI-series algorithms significantly outperform their base counterparts. In sparse data ($20\%$ training), HI-MD achieves a $0.8\%$-$4.4\%$ improvement in F1-score over MD while maintaining higher diversity (Diversity@20: 459 vs. 396 on MovieLens). For dense data ($80\%$ training), HI-BD improves F1-score by $2.3\%$-$5.2\%$ compared to BD, with diversity gains up to $18.6\%$. Notably, hybrid models consistently enhance novelty in sparse settings and exhibit robust parameter adaptability. The results validate that strategic hybridization effectively breaks the accuracy-diversity trade-off, offering a flexible framework for optimizing recommendation systems across data sparsity levels.
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